Quantitative identification of technological discontinuities using simulation modeling

TL;DR

This paper develops and tests metrics to quantitatively identify technological discontinuities within knowledge networks, demonstrating that persistence times of converging main paths effectively detect breakthrough innovations.

Contribution

The paper introduces five new metrics for identifying technological discontinuities and validates their effectiveness through simulation and empirical analysis.

Findings

Persistence times of converging main paths outperform other metrics in detecting discontinuities.

The best metric identified the designed discontinuity with 96-99% probability.

Empirical tests confirmed the simulation results, supporting the metric's applicability.

Abstract

The aim of this paper is to develop and test metrics to quantitatively identify technological discontinuities in a knowledge network. We developed five metrics based on innovation theories and tested the metrics by a simulation model-based knowledge network and hypothetically designed discontinuity. The designed discontinuity is modeled as a node which combines two different knowledge streams and whose knowledge is dominantly persistent in the knowledge network. The performances of the proposed metrics were evaluated by how well the metrics can distinguish the designed discontinuity from other nodes on the knowledge network. The simulation results show that the persistence times # of converging main paths provides the best performance in identifying the designed discontinuity: the designed discontinuity was identified as one of the top 3 patents with 96~99% probability by Metric 5 and it is, according to the size of a domain, 12~34% better than the performance of the second best metric. Beyond the simulation analysis, we tested the metrics using a patent set representative of the Magnetic information storage domain. The three representative patents associated with a well-known breakthrough technology in the domain, the giant magneto-resistance (GMR) spin valve sensor, were selected based on the qualitative studies, and the metrics were tested by how well the metrics identify the selected patents as top-ranked patents. The empirical results fully support the simulation results and therefore the persistence times # of converging main paths is recommended for identifying technological discontinuities for any technology.